The world of data and its many applications. This blog will help you learn how visionary companies are monetizing their data assets and utilizing external data to enhance business operations.

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Based on the structure and characteristics of your data product that you have determined in part 2, you will now be able to assess the cost to make your data available for sale. This cost serves as a price floor and gives you a starting point for pricing your data product.

This post is part of a four-part series on how to bring your data product to market with an effective go-to-market strategy. Download your copy of the free e-book Visionary Leader’s Guide to Data Monetization to read it in full.

Pricing your data product

The task of pricing your data product is a balance of art and science. Price it too high and the products won’t sell. Price it too low and you run the risk of leaving money on the table. In order to find the optimum price, consider what drives the value of your data product. At DataStreamX, we have identified three main factors that affect the value of data products, namely:

Richness – Data increases in value if it is accurate, robust, granular, detailed or frequent.

Reliability – Data increases in value if it is authentic, dependable, complete or consistent.

Rarity – Data increases in value if it is uncommon, irreplaceable or extraordinary.

These three factors affect how valuable your data product is to your consumers – and their willingness to pay for it. By understanding the value that your data holds, you are in a better position to price your data product accurately and confidently.

Bear in mind that the same dataset can be cut several ways and command different prices. For example, a company with data on vehicle traffic at gas stations across Europe may create one data product for each country or aggregate the data for all of Europe. Separately, the company may choose to provide real-time, weekly, monthly or yearly data on each of the gas stations that they are monitoring. With just these parameters alone, at least 200 data products could be created, each with a distinct value to the market.